Group Members: Travis, Ira, Micah
Goal: This dashboard presents a multi-regional study of U.S. weather behavior in 2024.
It combines exploratory weather analysis, geographical mapping, and predictive modeling, including a classification machine-learning model.
Exploring wind data using the Meteostat Python API:
Source: Meteostat Python API
Time Period: 2024
Frame: Hourly and Daily
Key Variableswspd: Average wind speed (mph)wdir: Mean wind direction (degrees)temp: Temperature (°F)coco: Condition code| Regional Wind Analysis by Speed and Direction | ||||
| Hourly Averages 2024 | Data: Meteostat | ||||
| latitude | longitude | Wind Statistics | ||
|---|---|---|---|---|
| Speed (mph) | Direction (°) | |||
| Midwest | ||||
| Cleveland, OH | 41.4993 | -81.6944 | 40.1 | 227.0 |
| Chicago, IL | 41.8781 | -87.6298 | 35.4 | 259.0 |
| Detroit, MI | 42.3314 | -83.0458 | 34.8 | 248.0 |
| Milwaukee, WI | 43.0389 | -87.9065 | 33.9 | 297.0 |
| Minneapolis, MN | 44.9778 | -93.265 | 28.2 | 307.0 |
| Northeast | ||||
| Buffalo, NY | 42.8864 | -78.8784 | 46.0 | 243.0 |
| Boston, MA | 42.3601 | -71.0589 | 38.2 | 278.0 |
| Philadelphia, PA | 39.9526 | -75.1652 | 31.0 | 297.0 |
| Pittsburgh, PA | 40.4406 | -79.9959 | 25.3 | 299.0 |
| New York, NY | 40.7128 | -74.006 | 23.8 | 300.0 |
| Southeast | ||||
| Jacksonville, FL | 30.3322 | -81.6557 | 32.5 | 81.0 |
| Miami, FL | 25.7617 | -80.1918 | 29.4 | 81.0 |
| Tampa, FL | 27.9506 | -82.4572 | 26.0 | 49.0 |
| Charlotte, NC | 35.2271 | -80.8431 | 22.7 | 319.0 |
| Atlanta, GA | 33.749 | -84.388 | 17.8 | 357.0 |
| West | ||||
| Denver, CO | 39.7392 | -104.9903 | 30.2 | 180.0 |
| San Francisco, CA | 37.7749 | -122.4194 | 29.9 | 294.0 |
| Los Angeles, CA | 34.0522 | -118.2437 | 24.6 | 208.0 |
| Portland, OR | 45.5152 | -122.6784 | 24.4 | 333.0 |
| Seattle, WA | 47.6062 | -122.3321 | 18.6 | 191.0 |
| Legend: 🔵North 🔴East 🟡South 🟢West | Darker = Stronger | ||||
the Goal of this model is to predict wind vectors at the following International Airports:
the features used to predict this are the locations and wind vectors associated with the 5 closest stations
We got the following recorded the following metrics on the test set:
R²: 0.7408853538672453
RMSE: 5.428108009372502
Model Objective - This model predicts weather condition codes using an extensive batch of features.
Condition Code Groupings1. How do weather patterns change by region?
2. What are some case studies of extreme weather?
3. How do geographical features (lakes, oceans, mountains, deserts, plains) impact weather patterns?